Non-parametric Mixed-Manifold Products using Multiscale Kernel Densities
© 2019 IEEE. We extend the core operation of non-parametric belief propagation (NBP), also known as multi-scale sequential Gibbs sampling, to approximate products of kernel density estimated beliefs that reside on some manifold. The original algorithm, though multidimensional, implicitly assumes the...
Main Authors: | Fourie, Dehann (Author), Teixeira, Pedro Vaz (Author), Leonard, John (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE,
2021-10-28T15:23:36Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Non-parametric Mixed-Manifold Products using Multiscale Kernel Densities
by: Fourie, Dehann, et al.
Published: (2021) -
Strong uniform consistency with rates for kernel density estimators with general kernels on manifolds
by: Wu, H.-T, et al.
Published: (2022) -
Bayesian learning for parametric and kernel density estimation
by: Nasios, Nikolaos
Published: (2006) -
Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation
by: Fourie, Dehann, et al.
Published: (2022) -
Non-parametric kernel density estimation-based permutation test: Implementation and comparisons.
by: Baranzano, Rosa
Published: (2011)